Vision foundation models are a new frontier in GeoAI research because of
their potential to enable powerful image analysis by learning and extracting
important image features from vast amounts of geospatial data. This paper
evaluates the performance of the first-of-its-kind geospatial foundation model,
IBM-NASA's Prithvi, to support a crucial geospatial analysis task: flood
inundation mapping. This model is compared with popular convolutional neural
network and vision transformer-based architectures in terms of mapping accuracy
for flooded areas. A benchmark dataset, Sen1Floods11, is used in the
experiments, and the models' predictability, generalizability, and
transferability are evaluated based on both a test dataset and a dataset that
is completely unseen by the model. Results show the impressive transferability
of the Prithvi model, highlighting its performance advantages in segmenting
flooded areas in previously unseen regions. The findings also suggest areas for
improvement for the Prithvi model in terms of adopting multi-scale
representation learning, developing more end-to-end pipelines for high-level
image analysis tasks, and offering more flexibility in terms of input data
bands.

评估 IBM-NASA 的 Prithvi 模型在洪水淹没区域的地理空间分析任务中的性能，并与其他卷积神经网络和视觉变换器架构进行比较，结果显示了 Prithvi 模型在未见过的区域中分割淹没区域的出色可迁移性，同时建议在多尺度表示学习、高级图像分析任务的端到端流程和输入数据波段的灵活性方面进一步改进。

评估 IBM 和 NASA 的地理空间基础模型在洪涝淹没区划中的应用

Assessment of IBM and NASA's geospatial foundation model in flood  inundation mapping

A common need for artificial intelligence models in the broader geoscience is
to represent and encode various types of spatial data, such as points (e.g.,
points of interest), polylines (e.g., trajectories), polygons (e.g.,
administrative regions), graphs (e.g., transportation networks), or rasters
(e.g., remote sensing images), in a hidden embedding space so that they can be
readily incorporated into deep learning models. One fundamental step is to
encode a single point location into an embedding space, such that this
embedding is learning-friendly for downstream machine learning models such as
support vector machines and neural networks. We call this process location
encoding. However, there lacks a systematic review on the concept of location
encoding, its potential applications, and key challenges that need to be
addressed. This paper aims to fill this gap. We first provide a formal
definition of location encoding, and discuss the necessity of location encoding
for GeoAI research from a machine learning perspective. Next, we provide a
comprehensive survey and discussion about the current landscape of location
encoding research. We classify location encoding models into different
categories based on their inputs and encoding methods, and compare them based
on whether they are parametric, multi-scale, distance preserving, and direction
aware. We demonstrate that existing location encoding models can be unified
under a shared formulation framework. We also discuss the application of
location encoding for different types of spatial data. Finally, we point out
several challenges in location encoding research that need to be solved in the
future.

本文从机器学习的角度提出了位置编码的概念，系统回顾并讨论了当前位置编码研究的现状和挑战，将已有的位置编码模型分类，并探讨了它们与不同类型空间数据的应用，并指出了未来位置编码研究面临的挑战。